The effect of migration on terror: Made at home or imported from abroad?
Bibliographic record
Abstract
Abstract We analyze how a country's immigrant population—defined as the stock of people born abroad—affects the probability of a terrorist attack in the host country. Using data for 20 OECD host countries and 183 countries of origin over the 1980–2010 period our OLS and 2SLS regressions show that the probability that immigrants from a specific country of origin conduct a terrorist attack in their host country increases with a larger number of foreigners from such countries living there. However, this scale effect does not differ from the effect domestic populations have on domestic terror. We find scarce evidence that terror is systematically imported from countries with large Muslim populations or countries where terror networks prevail. Policies that exclude foreigners already living in a country increase rather than reduce the risk that foreign populations turn violent, and so do terrorist attacks against foreigners in their host country. Highly skilled migrants are associated with a significantly lower risk of terror compared with low skilled ones, while there is no significant difference between foreign‐born men and women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".